ChatDeepSeek

Setup:

Install langchain-deepseek and set environment variable DEEPSEEK_API_KEY.

pip install -U langchain-deepseek
export DEEPSEEK_API_KEY="your-api-key"

Key init args — completion params: model: Name of DeepSeek model to use, e.g. 'deepseek-chat'. temperature: Sampling temperature. max_tokens: Max number of tokens to generate.

Key init args — client params: timeout: Timeout for requests. max_retries: Max number of retries. api_key: DeepSeek API key. If not passed in will be read from env var DEEPSEEK_API_KEY.

See full list of supported init args and their descriptions in the params section.

Instantiate:

from langchain_deepseek import ChatDeepSeek

model = ChatDeepSeek(
    model="...",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # api_key="...",
    # other params...
)

Invoke:

messages = [
    ("system", "You are a helpful translator. Translate the user sentence to French."),
    ("human", "I love programming."),
]
model.invoke(messages)

Stream:

for chunk in model.stream(messages):
    print(chunk.text, end="")

stream = model.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

Async:

await model.ainvoke(messages)

# stream:
# async for chunk in (await model.astream(messages))

# batch:
# await model.abatch([messages])

Tool calling:

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    '''Get the current weather in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

class GetPopulation(BaseModel):
    '''Get the current population in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

model_with_tools = model.bind_tools([GetWeather, GetPopulation])
ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls

See ChatDeepSeek.bind_tools() method for more.

Structured output:

from typing import Optional

from pydantic import BaseModel, Field

class Joke(BaseModel):
    '''Joke to tell user.'''

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: int | None = Field(description="How funny the joke is, from 1 to 10")

structured_model = model.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")

See ChatDeepSeek.with_structured_output() for more.

Token usage:

ai_msg = model.invoke(messages)
ai_msg.usage_metadata

{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}

Response metadata:

ai_msg = model.invoke(messages)
ai_msg.response_metadata